DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The supplemental reply filed on 8/28/2025 was not entered because supplemental replies are not entered as a matter of right except as provided in 37 CFR 1.111(a)(2)(ii). In the instant case, the supplemental reply does not reflect amendments to the claims of the instant application (17/972,513), and is instead directed to claim amendments of a different application (17/973,513) reflecting subject matter for treating Bell’s palsy with electrical acupuncture stimulators rather than AI analysis of sensor data to make treatment recommendations. Accordingly, the supplemental reply is not entered.
The status of the claims as of the response filed 8/14/2025 is as follows: Claims 1-31 remain cancelled. Claims 32-51 are currently amended and have been considered below.
Response to Amendment
Rejection Under 35 USC 112(a)
The claims have been amended such that they overcome some, but not all, of the 35 USC 112(a) rejections outlined in paras. 13-24 of the non-final rejection mailed 5/14/2025. See paras. 34-39 below for updated 35 USC 112(a) rejections.
Rejection Under 35 USC 112(b)
The claims have been amended to sufficiently clarify the indefinite language and limitations identified in the previous Office action, such that the corresponding 35 USC 112(b) rejections identified in paras. 25-28 of the non-final rejection are withdrawn.
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 103
The amendments made to the claims introduce limitations that are not fully addressed in the previous Office action, and thus the corresponding 35 USC 103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below.
Response to Arguments
Rejection Under 35 USC 101
On pages 23-24 of the response filed 8/14/2025 Applicant argues that the claims include various limitations that “cannot be examples of methods of economic principles or practices, managing personal relationships, or interactions between people” because they “involve interaction between a user, a plurality of biometric sensors, a wellness monitoring system, a plurality of databases, a plurality of processors configured with artificial intelligence, and at least one user device.” Applicant specifically points to a limitation for determining a treatment plan with a continually optimized artificial intelligence machine learning model that leans to recognize patterns across said plurality of users’ commonalities, by ratio, and extracts the output of the best treatment technique across said plurality of users’ with common ratios. Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that determining a treatment by learning patterns in previous users’ treatment outcomes as correlated with certain combinations (i.e. ratios) of input parameters could be accomplished by a human actor (e.g. a clinician) managing their personal behavior and/or interactions with others (e.g. a patient). For example, a clinician could observe sensor readings or other patient data for a current patient as well as several past patients with known outcomes, group them together by similar combinations (i.e. ratios) of characteristics, and observe patterns in the data to make determinations about which treatments resulted in the best outcomes for a current patient’s combination of characteristics as judged from the known outcomes of similar past patients. The clinician could then select the best treatment and suggest it to the current patient as the most effective option. Accordingly, this step does fit into the category of “certain methods of organizing human activity” and is found to recite an abstract idea. The use of an artificial intelligence machine learning model that is continually optimized via machine learning to achieve such a function amounts to instructions to “apply” the abstract idea using high-level computing components such that this otherwise-abstract step is digitized and/or automated in an electronic environment, and thus does not provide integration into a practical application.
On pages 24-25 of the response Applicant appears to argue that the claims are eligible because they are similar to claim 1 of SME Example 39, which is directed to a method of training a neural network for facial detection that was found not to recite any judicial exception. Applicant’s arguments are fully considered, but are not persuasive. The claim at issue in Example 39 was directed to a method of training a neural network for facial detection that involved the collection and transformation of digital facial images to create two training sets for the neural network trained in two stages. Though this claim was not found to recite an abstract idea at all, it differs from the claims of the instant application due to the nature of the collected and transformed data as well as the increased specificity of the resulting trained model. That is, the claim of Example 39 recited the collection and transformation of digital facial images to create training sets, which could not reasonably be accomplished by a human actor mentally or by following instructions for managing personal behavior, whereas the instant claim merely recites that the biometric monitor is trained via an artificial intelligence machine learning model which determines ratios of physiological and mental state wellness parameters between users and determines which treatment options are performing more effectively than others based on the ratios, which a human actor could achieve by recognizing patterns in user data as explained above. Further, Example 39 is directed to the training of specifically a neural network using two distinct training sets in two separate training stages, whereas the instant claim merely recites general use of an artificial intelligence machine learning model, which could encompass a wide array of model architectures, training techniques, methods of iterating or tuning the system, etc. Finally, Example 39 was directed only to a method of training a neural network via the creation and use of training datasets, and did not include further steps that could be characterized as aspects of a certain method of organizing human activity. In contrast, the instant claims do include steps that can be reasonably characterized as “certain methods of organizing human activity” (e.g. monitoring wellness parameters, identifying signatures or patterns in the data, generating and periodically updating reference points for a patient, searching databases to compare data for user commonalities, analyzing and processing health data, determining patients with similar input ratios/combinations and determining which treatments are most effective for each ratio, generating and tailoring personalized wellness suggestions for a user, tracking a user’s productivity and performance in work and daily life, assisting regulation of a user’s dopamine levels by suggesting optimal levels of social media usage, comparing current data with prior data, tracking a user’s reaction to an external stimulus like audio, visual, or meditation content, generating question prompts for a user, providing recommendations to a user in an alert, etc.). Accordingly, the claim of Example 39 does not recite an abstract idea, while the independent claims of the instant application do.
On pages 25-27 of the response Applicant argues that the claimed invention is similar to claim 19 of SME Example 2 because it does not “merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet.” Applicant’s arguments are fully considered, but are not persuasive. The subject matter of Example 2 is directed to retaining visitors to a website via particular web-based technical steps that would not be relevant to analog business because they are rooted in a new computer technology (websites). In contrast, the present invention is directed to the collection and analysis of patient data for the purpose of wellness recommendations and alerting, which is not a business challenge particular to the Internet or another computer technology. Rather, the present claims merely utilize computing components (e.g. databases, a user device, a biometric monitor, a programmable electronic system, and an artificial intelligence machine learning model) to automate and/or digitize the evaluation of sensor data for the purpose of evaluating health status and making personalized treatment recommendations that frequently takes place during analog patient-clinician interactions. Accordingly, the instant claims are not analogous to those of Example 2 and are not found to be patent eligible.
On pages 27-28 of the response, Applicant analogizes the instant claims to those found eligible in Example 42 because the invention provides “a holistic medical approach whereby the combination of said plurality of sensor type data collections, artificial intelligent analysis, personal life metrics, patient information, and streamlined recommendations with real-time data of efficacy are entirely concrete and patent eligible features of the Applicant’s invention.” Applicant’s arguments are fully considered, but are not persuasive. First, Examiner notes that Applicant has not provided any explanation of why the analysis of the eligible claim in Example 42 would be similar to the analysis for the independent claims of Applicant’s invention. The present claims differ from those of Example 42 at least because they do not provide remote format standardization with updated messaging capabilities; in Example 42, the crux of the invention was a technical improvement related to specific formatting conversions and remote accessibility. In contrast, the crux of the instant claims is analysis of sensor data for health status detection and treatment recommendation, and they do not include any discussion of technical formatting or remote accessibility features. Evaluating many different types of patient data together in a holistic manner to provide streamlined recommendations about treatment efficacy is not a technical improvement to a compute or other technical field, and could instead be achieved by a human actor such as a clinician managing their personal behavior and/or interactions with others such as a patient, as explained above. The use of computerized infrastructure like various sensors, a biometric monitor, electronic databases, an AI/ML analysis amounts to mere instructions to “apply” these otherwise-abstract functions in an electronic environment and do not confer eligibility.
On page 30 of the response Applicant argues that the claimed invention is integrated into a practical application because it “is directed to a particular improvement in a wellness promoting system by allowing for continuous monitoring of physiological and mental data and artificial intelligence of said collected physiological and mental state data to determine suggestions and recommendations for preventative care and treatment regardless of the presence of a physiological or mental health condition, offered in real-time.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that wellness promotion is not a technical field, and any improvement to methods for promoting health or wellness amounts to an improvement to the abstract idea itself, which does not confer eligibility. Applicant’s arguments that the present invention amounts to a particular improvement because “without the claimed invention, quickly and efficiently determining the most effective care plan for improving an individual’s overall wellness and providing treatment based on said care plan remains inaccessible and is lacking in preventative care” are not persuasive because the additional elements are merely utilized to digitize and/or automate otherwise-abstract data processing and analysis steps, and “‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept” as noted in MPEP2106.05(f)(2). Examiner notes Pg1 L15-17 of Applicant’s specification, which states that “AI allows these therapeutic practices to be constantly monitored and updated, taking away the need of a human, and allowing for more vulnerability and honesty,” showing that the invention seeks to utilize the improved speed and efficiency of computing technologies like AI to automate medical monitoring and treatment suggestion functions that could otherwise be accomplished by a human actor such as a medical professional.
On pages 30-31 of the response Applicant argues that “the claimed invention is inextricably tied to computer technology” because it “relies on biometric data collection via a plurality of sensors” and “incorporates an artificially intelligent machine learning model which periodically generates reference points for said user based on said plurality of real-time physiological and mental state data parameters as collected by said plurality of sensors which are consistently updated for progress tracking purposes” and “identifies patterns, trends, and commonalities across said users by applying a ratio approach to computer the most similar and most effective treatment approach from comparing said user’s prior treatment efficacies as determined from said plurality of real-time physiological and mental state data parameter collection.” Applicant’s arguments are fully considered, but are not persuasive. The use of a plurality of sensors to collect data amounts to insignificant extra-solution activity in the form of necessary means of data gathering because the sensors are merely utilized as methods of obtaining the patient data needed for the main data processing and analysis steps of the invention (see MPEP 2106.05(g)). Further, merely utilizing computer technology (e.g. high-level artificial intelligence implemented by a nonspecific biometric monitor) does not provide integration into a practical application, and the overall effect of the additional elements of the independent claims is to digitize/automate otherwise-abstract functions like data processing and analysis as explained in more detail above. The claims do not provide a technical improvement to a computer or other technical field, and thus do not provide an inventive concept.
On pages 31-32 of the response Applicant argues that the use of artificial intelligence analysis of biometric data including periodically generating reference points for a user and simultaneously comparing data points across users to reach consensus in the most effective treatment options “integrate the alleged abstract idea into a specific platform for inventory management that is operable to use supply and demand corresponding to a wellness promoting system.” Applicant’s arguments are fully considered, but are not persuasive. As explained above, periodically generating reference points for use in tracking patient progress as well as comparing data points across multiple users to recognize patterns in the most effective treatment types is part of the abstract idea because a human actor could reasonably perform these steps during patient-clinician interactions. Use of artificial intelligence to perform these functions amounts to the words “apply it” with a computer. Accordingly, these elements do not provide integration into a practical application. Further, Examiner notes that there is no mention of inventory management or supply and demand in the claims or specification, and these arguments are unpersuasive.
On page 33 of the response Applicant appears to argue that because a static price index was found to be an inventive concept in Trading Technologies v. CQG, the instant claims also provide significantly more than the judicial exception. Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that no parallels have been drawn between the eligible claims of Trading Technologies and the instant claims, and it is unclear what specific limitations Applicant is alleging as providing an inventive concept or something other than what is well-understood, routine, and conventional activity in the field; accordingly, this line of argument amounts to a mere allegation of patentability and is unpersuasive.
On page 27 of the response Applicant argues that “the claimed invention does significantly more than recite the alleged abstract idea because the claimed invention incorporates the user of has a specific intended use for the artificial intelligence machine learning model in specifically utilizing the ratios of said plurality of users said real-time physiological and mental state data wellness parameters and utilizing this information to identify commonalities across said users in said plurality of databases to identify the most tailored, likely effective treatment plan.” Examiner notes that the underlying functions of utilizing ratios of user data to identify commonalities across different users to identify the most tailored, likely effective treatment plan is part of the abstract idea itself (as explained above), and thus they cannot provide “significantly more” than the abstract idea and do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”) The use of artificial intelligence to achieve these otherwise-abstract functions amounts to instructions to “apply” the abstract idea (as explained above), and also does not confer eligibility.
For the reasons outlined above, the 35 USC 101 rejections for claims 32-51 are upheld.
Rejection Under 35 USC 103
On pages 35-36 of the response Applicant argues that the combination of Rajput and Francois “fails to teach the key component of Applicant’s invention wherein the system utilizes said artifial intelligence learning model to determine the patterns and trends, via ratios of said plurality of physiological and mental state wellness parameter similarities, between users in said plurality of databases to determine the most effective treatment option based on correlations between said users.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Rajput does teach this limitation; at least para. [0135] describes use of AI and machine learning models to find patterns across different cohorts of similarity-matched patients (i.e. patients with commonalities in input data combinations/ratios) and identify wellness recommendations known to be successful.
On page 36 of the response Applicant argues that “the specific varied sensor types and specific data collected from each and real-time aspect of data collection are entirely novel.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Rajput does teach at least two of the claimed plurality of sensor types collecting respective data types, including:
facial expression data obtained from said camera (Rajput [0127], [0129], noting collected patient data can include facial expressions obtained from cameras);
body language data obtained from said biometric sensor (Rajput [0019], [0100], [0127], [0129], noting collected patient data can include finger tapping and finger movement/swipes obtained from a touch screen as well as movements obtained from an accelerometer and/or gyroscope (i.e. body language data obtained from biometric sensors));
emotional state data obtained from at least one of the following: said sweep sensor, said molecular sensor, and said biosensor (Rajput [0107], [0127]-[0128], noting collected patient data can include mood (i.e. emotional state data) obtained via camera based systems (i.e. sweep sensors) or wearable devices (i.e. biosensors));
voice stress data obtained from said microphone (Rajput [0017], [0115]-[0121], noting collected patient data can include acoustic voice characteristics like volume, pitch, and speed (i.e. voice stress data); see also [0077] & [0175], noting the system includes a microphone, indicating that the acoustic data is collected via the microphone); and
haptic data obtained from said haptic sensor (Rajput [0098], [0100], [0127], [0129], noting collected patient data can include finger tapping and finger movement/swipes (i.e. haptic data) collected via a touch screen of a user computing device (i.e. a haptic sensor)).
On page 36 of the response Applicant argues that “features such as monitoring hormone levels via said biosensor in connection to regulating media for optimal dopamine and monitoring fluctuations of homeostasis to trigger therapy techniques also add to the distinction of Applicant’s invention.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Rajput adequately teaches these features of the claims, including:
assist regulation of said user's dopamine levels in response to social media usage via analysis of patterns of said social media usage cross-referenced with analysis of said physiological and mental state data in real-time and thereby recommend optimal levels of usage to correspond to optimal dopamine levels tracked via said plurality of sensors (Rajput [0107], [0113]-[0114], noting the system can show a patient personalized pictures/videos to remind them of past happy moments and elicit positive emotions (i.e. assist regulation of dopamine levels, since dopamine is correlated with happy or positive emotions); the system includes a social media module where family and friends may contribute photos and videos and communicate with the patient to help reduce feelings of loneliness and isolation in the patient as noted in [0159], such that the caregiver-provided inputs of [0113] are considered equivalent to social media usage. Data on mood, emotions, speech, vocal reactions, typing patterns, etc. are collected and analyzed to determine features like positive/negative sentiment ratio, acoustic voice characteristics, and other reactions to the presented media as noted in [0115]-[0121] for the purpose of optimizing the treatment provided to the patient. Taken together, all of these disclosures show that the system can analyze patterns of social media usage (e.g. engagement with the personalized pictures/video contributed by friends, family, and caregivers) in combination with analysis of said physiological and mental state data in real time (e.g. data on mood, emotions, speech, vocal reactions, typing patterns, sentiment ratio, etc.) to select personalized content that will elicit happy or positive emotions for the user (e.g. assist regulation of dopamine levels to an optimal level as tracked via the physiological and mental state data)); and
employ said at least one sensor to track said user's reaction to a third-party input; wherein a third-party input is defined as an external stimulus triggering said user to react and generate abnormalities of homeostasis of the body thereby generating novel physiological and mental state data; whereby the third-party input comprises at least one of the following: musical, visual, and auditory input, coping skill sets, meditation techniques, supplements, exercise, and suggested drugs with physicians consent (Rajput [0124], noting the system utilizes sensors like the wearable device, an electrode, and/or a camera to capture the patient’s real-time reaction to a meditation and mindfulness activity (i.e. a third-party input intended to trigger a reaction that alters the user’s current state so that new physiological and mental state data may be obtained); see also [0089]-[0091], noting the system tracks accuracy of the user in response to displayed photos/videos submitted by a caregiver or friends (i.e. visual third-party input)).
On pages 36-37 of the response Applicant argues that “the combination of Rajput et al. and Francois and the prior art in general would be unsatisfactory for its intended purpose, because it frustrates the purpose of Applicant’s invention.” Applicant’s arguments are fully considered, but are not persuasive. Applicant has provided no specific reasoning regarding particular features of Rajput and Francois (or any other prior art references) that would frustrate the operation of the combined system. Examiner notes that the only feature not present in Rajput that Francois is relied upon to disclose is the encryption of stored data; Applicant has provided no reasoning or evidence about why modifying the patient data storage features of Rajput to include data encryption functions as in Francois would frustrate or render the system inoperable, and Examiner maintains that one of ordinary skill in the art would have been motivated to make such a modification for the purpose of enhancing the security of sensitive data and conforming to applicable health data storage standards (as suggested by Francois [0481], and as explained in para. 52 of the non-final rejection mailed 5/14/2025).
On page 37 of the response, Applicant argues that “Examiner’s suggestion that it would be obvious to combine [Rajput and Francois] is improper, because it relies on improper hindsight reasoning. Applicant’s arguments are fully considered, but are not persuasive. It must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On pages 37-38 of the response, Applicant argues that Rajput and Francois and the other prior art references “lack any specific teachings, suggestions, or motivation for combination to achieve Applicant’s invention as claimed and now amended.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Rajput itself discloses many of the newly-amended features (as explained above), and that an explicit motivation to combine the system of Rajput with the data encryption features of Francois has been provided in both para. 52 of the non-final rejection, as well as para. 67 below.
On page 38 of the response, Applicant argues that Rajput and Francois “both teach away from Applicant’s as claimed, because the usefulness of Applicant’s invention was not even remotely appreciated.” Applicant’s arguments are fully considered, but are not persuasive. There is no specific evidence or reasoning provided about features of Rajput or Francois that allegedly teach away from the instant invention, and Examiner notes that ‘appreciation of the usefulness of the instant invention’ is not a standard for determining whether a prior art reference teaches away from the invention.
On page 39 of the response Applicant argues that Rajput discusses a patient clinical database “without mention of using artificial intelligence models to find patterns across patients and use this information to enable machine learning of patient preferences and successful medical wellness recommendations.” Applicant contends that “Rajput’s use of AI extends solely to estimation and generating alerts omitting the pattern identification and tailored output specifications of the Applicant’s invention and instead only serving numerical purposes.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that at least para. [0135] of Rajput provides discussion of the use of AI and machine learning models to find patterns across different cohorts of similarity-matched patients (i.e. patients with commonalities in input data combinations/ratios) and identify wellness recommendations known to be successful; relevant portions are reproduced below:
For example a table (or similar data structure) of normed performance data may be collected/extracted for similar patients controlled for age, education, career, socio-economic, and, physiological, health and historical factors. This could be used by the AI/ML to determine how the performance of the patient compares to similar (matched) patients to determine how their state compares to what their maximal or minimal predicted cognitive range would be. For example, an 80 year old would not be expected to be able to respond as quickly as a 50 year old (indicating a slowed processing speed), and may also be expected to recall fewer details (indicating less precise memory). However by first selecting a matched patient cohort, the relative performance of the patient can be assessed and used to make predictions of expected progress (so treatment can be adjusted if it fail to meet expected progress)…. In this embodiment the cognitive analytics engine 40 generates multiple groups of similar patients using the data in the patient clinical database 10 (i.e. generate multiple patient cohorts), and then generate a AI/ML assessment model for each group/patient cohort to generate a set of AI/ML models. During analysis of data from the digital cognitive therapy delivery module, the cognitive analytics engine 40 then matches the patient to one of the patient cohorts, and uses the associated AI/ML model for that group to assess the patient state and performance. In some embodiments the cognitive analytics engine 40 may estimate the specific side effects or combination of impairments the patient is suffering from and generate or select a cohort of patients with similar side effects or impairments. This may be used to assess the relative state/performance of the treatment or to identify potential treatments based on identifying the treatments common to the top responding patients in the cohort, e.g. by using a quantile regression or similar model to compare the treatments of the top 10% vs the middle 50% of patients.
On page 39 of the response Applicant argues that Francois fails to teach a “tool to accumulate patterns autonomously or to use this information to shape future health recommendations.” Applicant’s arguments are fully considered, but are moot because the Francois reference is not relied upon to teach this feature of the claims.
On pages 39-40 Applicant argues that Rajput merely “utilizes ‘wearable and medical devices’ but does not mention the combination of any of the following: camera, biometric sensor, microphone, or haptic sensor” such that “the data collected in Rajput et al. is constrained to that collected from wearable devices and patient data imported from electronic health records” which is “fundamentally different from real-time sensors and the various types of data collected from these sensors.” Applicant’s arguments are fully considered, but are not persuasive. As explained in para. 18 above, Rajput does disclose each of these five sensor types and respective collected data types, though the instant claims only require at least two of such sensor types.
On page 40 of the response Applicant argues that Rajput “does not describe real-time DNA monitoring” or use of such information “to measure real-time progress as in how the Applicant’s invention utilizes the incorporated DNA analysis.” Applicant further asserts that “there is not an obvious connection between DNA analysis and real-time treatment efficacy progress measuring nor is it disclosed the method by which this could be achieved nor a plurality of metrics such as changes in DNA sequencing, measuring hormones, or chemical releases from fight or flight scenarios.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that the claims as presently drafted do not recite “real-time DNA monitoring” nor use of DNA analysis for “real-time treatment efficacy progress measure.” Rather, claim 51 merely states that “said molecular sensor is configured to monitor and detect harmful hormonal changes to molecular health and DNA structure of said user by monitoring biomarkers and observing patterns of various DNA strands via genome sequencing and aid of said molecular sensor analyzing nanopores and ionic changes, and wherein said system is further configured to notify said user if hormonal changes are detected and provide updated suggestions for hormonal optimization.” Thus, all that is required to meet the claim language is a molecular sensor that can monitor biomarkers and observe patterns in DNA strands via genome sequencing and analysis of nanopores and ionic changes, whereby the system notifies a user if hormonal changes are detected and provides updated suggestions for hormone optimization. Examiner submits that Rajput does disclose monitoring molecular data such as DNA to determine patterns and provide treatment suggestions to a patient (Rajput abstract, [0042]-[0044], [0135], noting the system processes the collected patient data (including molecular data like protein and genetic test results per [0175] & [0191]) to detect changes in patient status or harmful side effects (considered equivalent to harmful hormonal changes to molecular health and DNA structure when the analyzed data includes protein, genetic, and other blood test results as indicated above); see also [0191], noting genetic analysis results include alleles that are linked to neurodegenerative disorders, considered equivalent to observing patterns (i.e. health condition linkages) of various DNA strands (i.e. alleles)). However, Examiner concedes that Rajput does not explicitly disclose a molecular sensor analyzing nanopores and ionic changes, such that a new reference is relied upon below to remedy this deficiency.
On page 40 of the response Applicant argues that “neither [Rajput nor Francois] discuss the use of DNA genomic sequencing changes nor molecular sensors as a metric for said user” nor is there discussion “of everyday activities such as playing with children or animals.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that there is no recitation of monitoring playing with children or animals in the claims, such that this line of argument is unpersuasive. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Further, Examiner submits that Rajput does discuss genetic analysis of DNA such as alleles to discover patterns or harmful indications, as explained in the preceding paragraph.
On page 41 of the response Applicant argues that Rajput “does not describe the regulation of content as intended for optimizing dopamine production.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Rajput does provide and recommend content for the purpose of eliciting positive or happy emotions (see [0107], [0113]-[0114]), which is considered equivalent to assisting with regulating dopamine because dopamine levels are correlated with happy or positive emotions.
On page 41 of the response Applicant argues that Rajput “does not discuss using the magnetic field as a method to track changes in the human body as triggers for action.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that the claims do not recite tracking or utilizing a magnetic field. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
On page 41-42 of the response Applicant argues that Rajput and Francois do not “discuss tracking of neurological hemisphere activity as pertaining to visual, auditory, tactile inputs from the user’s everyday life to train an artificial intelligence model to determine user preferences.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that the claims do not recite tracking neurological hemisphere activity as pertaining to these three types of inputs for the purpose of training an artificial intelligence model to determine user preferences. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). At most, the independent claims recite employ said at least one sensor to track said user's reaction to a third-party input; wherein a third-party input is defined as an external stimulus triggering said user to react and generate abnormalities of homeostasis of the body thereby generating novel physiological and mental state data; whereby the third-party input comprises at least one of the following: musical, visual, and auditory input, coping skill sets, meditation techniques, supplements, exercise, and suggested drugs with physicians consent, which Examiner submits is taught by para. [0124] of Rajput, noting the system utilizes sensors like the wearable device, an electrode, and/or a camera (i.e. at least one sensor) to capture the patient’s real-time reaction to a meditation and mindfulness activity (i.e. a third-party input intended to trigger a reaction that alters the user’s current state so that new physiological and mental state data may be obtained)).
On page 42 Applicant argues that “while Rajput in view of Francois and Buco collect medication data they do not compound such with other biometrics to generate reference points and correlate with other users as in the Applicant’s key component of the invention in detecting correlations of users across said plurality of databases and utilizing periodically generated reference points of progress and efficacy to identify the correlations.” Applicant’s arguments are fully considered, but are not persuasive. Rajput does disclose collecting medication data and using such data in combination with the many other types of data collected for a user for the purpose of establishing patient profiles with baseline reference points (see [0080]-[0082]) that can be used to track a given user’s progress as well as to detect correlations of different users, e.g. by matching cohorts of similar patients and evaluating performance and progress metrics within the cohorts (see [0135]).
On page 42 Applicant argues that Weiss does not teach use of vagus nerve stimulation “in conjunction with technology intended to provide an artificial intelligence machine learning model biofeedback and neural feedback to assess its efficacy.” Applicant’s arguments are fully considered, but are moot because Weiss is not relied upon to disclose such features. Applicant further asserts that though Rajput “discusses using EEG information to determine the user’s mental state it does not expand to hormone production information nor use that as a trigger for prescribing a certain type of therapy such as vagus nerve stimulation.” Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that the claims do not recite using hormone production information to trigger prescription of a certain type of therapy such as vagus nerve stimulation. Rather, claim 44 merely recites that “said recommendation includes suggestions for vagus nerve stimulation therapy sessions” with no specific description of how that particular type of therapy is triggered for recommendation. Rajput describes many types of treatment recommendations that may be monitored for real-time cranial feedback via EEG (see [0124]), and further contemplates capturing and characterizing biomarkers like autonomic nervous system functioning and sympathovagal balance (Rajput [0126], [0128]), but fails to explicitly disclose that the suggested and monitored therapy is specifically vagus nerve stimulation therapy. However, Weiss teaches that an analogous system analyzing sensor data and providing therapy recommendations can include suggestions for vagus nerve stimulation therapy sessions intended to elicit a particular response in the user (Weiss [0018]-[0021]). Examiner maintains that it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the treatment suggestions and monitored treatment response of the Rajput to include suggestions for and monitoring of vagus nerve stimulation therapy sessions as in Weiss because the stimulation of the vagus nerve can inhibit adverse patient conditions, reduce harmful symptoms, and provide a stress relief/calming effect on a patient such that recommending and monitoring this type of treatment would be beneficial for treating an identified patient condition (as suggested by Weiss [0020]-[0021], and as analogous to the suggested treatment types of Rajput [0124]).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 32-51 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 32 recites “a biometric monitor… configured to… identify signatures across all of said plurality of sensors and predict a wellness therapy via artificial intelligence analysis provided via one or more processors, wherein said analysis comprises:… searching said plurality of databases to compare said real-time physiological and mental state data of said user to identify common patterns and trends between said user and a plurality of users in said plurality of databases, wherein commonalities are defined by shared characteristics across said real-time physiological and mental state data” in lines 17-29. The claim further recites “wherein said biometric monitor is trained via an artificial intelligence machine learning model which determines ratios of said plurality of physiological and mental state wellness parameters, between users in said plurality of databases” in lines 44-48 and “whereby said artificial intelligence machine learning model learns to recognize patterns across said plurality of users’ commonalities, by ratio” in lines 56-58. However, Applicant’s specification does not adequately describe exactly how “artificial intelligence analysis” is employed to “identify signatures across all of said plurality of sensors,” “identify common patterns and trends,” “determine ratios of said plurality of physiological and mental state wellness parameters,” and “recognize patterns across said plurality of users’ commonalities, by ratio” in a manner that would convince one of ordinary skill in the art that Applicant had possession of these features at the time of filing. At most, the specification makes various functional descriptions of suggesting treatments based on data collected from a user and “previous users with similar profiles” (see at least Pg2 L1-7, Pg6 L7-23, Pg10 L23-26, Pg11 L15-16, Pg21 L2-10) and use of “ratios” to determine which therapies are working better than others (see at least Pg6 L13-17, Pg18 L10-14). However, there is no disclosure of how the real-time physiological and mental state wellness parameters are actually analyzed with any particular algorithms or techniques to find any specific type of similarities, commonalities, patterns, or trends, nor how “ratios” are determined or evaluated as being the same or similar between patients. The invocation of generic “artificial intelligence” or “machine learning” for these purposes throughout the specification (see sections cited above) amounts to disclosure of a “black box” that does not provide any specific details regarding the algorithm, training, structure, learning capabilities, inputs, outputs, transformations, etc. of this element that would convince one of ordinary skill in the art that Applicant had possession of any particular methods of similarity/pattern/ratio/trend identification in the manner recited in this claim. Accordingly, the written description does not provide sufficient support to convey to one of ordinary skill in the art that Applicant had possession of the claimed subject matter, and this claim is rejected under 35 U.S.C. 112(a). Claims 38 and 46 recite substantially similar subject matter as claim 32 such that they are also rejected on this basis.
Claim 32 recites “employ said at least one sensor to track said user’s reaction to a third-party input.” The original disclosure only appears to describe tracking a user’s reaction to music, video, or a person talking via “sweep sensors” as in Pg15 L9-11. There is no description of how “said at least one sensor” of claim 32 (e.g. two or more of a biometric sensor, a biosensor, a camera, a microphone, or a haptic sensor as in lines 2-9) would perform the claimed tracking step. Accordingly, this limitation lacks sufficient written description and is rejected under 35 USC 112(a).
Claim 32 recites “generate question prompts modeled from therapeutic questioning tests based on a user’s collected biometric data to prompt said user, extract emotional state data, narrow diagnosis for said user and analyze user responses via said plurality of sensors to said question prompts to determine further question generations to continually improve diagnosis accuracy” in lines 92-96. However, Applicant’s specification does not adequately describe exactly how the programmable electronic system actually generates question prompts to prompt the user, extract emotional state data, narrow diagnosis for the user, and analyze user responses to determine further question generations in a manner that would convince one of ordinary skill in the art that Applicant had possession of these features at the time of filing. At most, the specification makes various functional descriptions of AI asking questions to a user “in order to tell their future or… diagnose them” and analyzing the reactions “to determine the user’s true feelings” so that another question may be asked (see at least Fig. 5, Pg9 L26 – Pg10 L13, Pg16 L18-24), but there is no description of how the system comes up with the initial question prompts, how emotional state or diagnoses are determined, nor how the user responses are actually analyzed and used to “determine further question generations.” That is, there is no disclosure of how question prompts are formulated either initially or in response to analyzed user responses, nor how the responses are actually analyzed. The invocation of generic “artificial intelligence” for this purpose throughout the specification (see sections cited above) amounts to disclosure of a “black box” that does not provide any specific details regarding the algorithm, training, structure, learning capabilities, inputs, outputs, transformations, etc. of this element that would convince one of ordinary skill in the art that Applicant had possession of a particular method of generating question prompts and further questions in the manner recited in this claim. Accordingly, the written description does not provide sufficient support to convey to one of ordinary skill in the art that Applicant had possession of the claimed subject matter, and this claim is rejected under 35 U.S.C. 112(a). Claims 38 and 46 recite substantially similar subject matter as claim 32 such that they are also rejected on this basis.
Claims 33-37, 39-45, and 47-51 are also rejected on the above bases identified for claims 32, 38, and 46, respectively, because they inherit the unsupported limitation due to their dependence on claims 32, 38, and 46, respectively.
Claims 37, 42, and 50 each recite “wherein user-specific data stored within said plurality of databases includes family history, genealogy, genetic information, environmental information, financial information, career information, and lifestyle information of said user, and wherein said user-specific data is analyzed via artificial intelligence by pattern identification of commonalities across said user-specific data within said plurality of databases to determine a wellness plan for said user guided by previous wellness plan successes for similar user-types, as categorized by said user-specific data.” However, Applicant’s specification